Open Health Data Latest Articleshttps://openhealthdata.metajnl.com/articles/Latest articles published by Open Health Dataen-usSat, 25 May 2019 14:26:52 -0000“Using Crowd-Sourced Data to Explore Police-Related-Deaths in the United States (2000–2017): The Case of Fatal Encounters”https://openhealthdata.metajnl.com/article/10.5334/ohd.30<p><strong>Objectives:</strong> We evaluated the Fatal Encounters (FE) database as an open-source surveillance system for tracking police-related deaths (PRDs).</p><p><strong>Methods:</strong> We compared the coverage of FE data to several known government sources of police-related deaths and police homicide data. We also replicated incident selection from a recent review of the National Violent Death Reporting System.</p><p><strong>Results:</strong> FE collected data on <em>n = 23,578</em> PRDs from 2000–2017. A pilot study and ongoing data integration suggest greater coverage than extant data sets. Advantages of the FE data include circumstance of death specificity, incident geo-locations, identification of involved police-agencies, and near immediate availability of data. Disadvantages include a high rate of missingness for decedent race/ethnicity, potentially higher rates of missing incidents in older data, and the exclusion of more comprehensive police use-of-force and nonlethal use-of-force data—a critique applicable to all extant data sets.</p><p><strong>Conclusions:</strong> FE is the largest collection of PRDs in the United States and remains as the most likely source for historical trend comparisons and police-department level analyses of the causes of PRDs.</p> Published on 2019-05-07 11:41:05https://openhealthdata.metajnl.com/article/10.5334/ohd.30Seasonal Abundance of Fecal Indicators and Opportunistic Pathogens in Roof-Harvested Rainwater Tankshttps://openhealthdata.metajnl.com/article/10.5334/ohd.29<p class="p1">Here we provide seasonal data on the concentrations of total coliform, <em>Escherichia coli</em> and <em>Enterococcus</em> spp. and six opportunistic pathogens (<em>Acanthamoeba</em> spp., <em>Legionella</em> spp., <em>Legionella pneumophila</em>, <em>Mycobacterium avium</em>, <em>Mycobacterium intracellulare</em>, and <em>Pseudomonas aeruginosa</em>) of public health significance in 24 tank water samples over six monthly sampling events from August 2015 to March 2006. Quantitative PCR (qPCR) assays were chosen for the quantification of six opportunistic pathogens and culture-based methods were used for the enumeration of fecal indicators. The data fle has been stored in a publicly available repository. The data on concentrations of opportunistic pathogens in RHRW will provide information for rainwater users regarding potential seasonality of risks. Quantitative data presented in this study can be used to perform quantitative microbial risk assessment (QMRA) of RHRW for various potable and nonpotable uses. Data can be used by health regulators to develop guidelines related to RHRW.<span class="Apple-converted-space"> </span></p><p class="p1"> </p><p class="p2"><strong>Funding statement: </strong>This research was undertaken and funded as part of a Fulbright-CSIRO Postgraduate Scholarship sponsored by the CSIRO Land and Water Flagship.</p><p class="p1"><span class="Apple-converted-space"><br /></span></p> Published on 2018-07-03 17:46:36https://openhealthdata.metajnl.com/article/10.5334/ohd.29VBORNET gap analysis: Mosquito vector distribution models utilised to identify areas of potential species distribution in areas lacking records.https://openhealthdata.metajnl.com/article/10.5334/ohd.27<p class="p1"><span class="s1">This is the second of a number of planned data papers presenting modelled vector distributions produced originally during the ECDC funded VBORNET project. This work continues under the VectorNet project now jointly funded by ECDC and EFSA. Further data papers will be published after sampling seasons when more field data will become available allowing further species to be modelled or validation and updates to existing models. </span></p><p class="p2"><span class="s1">The data package described here includes those mosquito species first modelled in 2013 &amp; 2014 as part of the VBORNET gap analysis work which aimed to identify areas of potential species distribution in areas lacking records. It comprises three species models together with suitability masks based on land class and environmental limits. The species included as part of this phase are the mosquitoes <em>Aedes vexans</em>, <em>Anopheles plumbeus</em> and <em>Culex modestus</em>.</span></p><p class="p2"><span class="s1">The known distributions of these species within the area covered by the project (Europe, the ­Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) assistance in ­targeting surveys to collect distribution data for those areas with no field validated information, and b) a first indication of the species distributions within the project areas.</span></p> Published on 2016-12-19 16:32:46https://openhealthdata.metajnl.com/article/10.5334/ohd.27VBORNET Gap Analysis: Sand Fly Vector Distribution Models Utilised to Identify Areas of Potential Species Distribution in Areas Lacking Recordshttps://openhealthdata.metajnl.com/article/10.5334/ohd.26<p class="p1"><span class="s1">This is the first of a number of planned data papers presenting modelled vector distributions, the models in this paper were produced during the ECDC funded VBORNET project. This work continues under the VectorNet project now jointly funded by ECDC and EFSA. This data paper contains the sand fly model outputs produced as part of the VBORNET project. Further data papers will be published after sampling seasons when more field data will become available allowing further species to be modelled or validation and updates to existing models.</span></p><p class="p2"><span class="s1">The data package described here includes those sand fly species first modelled in 2013 and 2014 as part of the VBORNET gap analysis work which aimed to identify areas of potential species distribution in areas lacking records. It comprises four species models together with suitability masks based on land class and environmental limits. The species included within this paper are <em>Phlebotomus ariasi</em>, <em>Phlebotomus papatasi</em>, <em>Phlebotomus perniciosus</em> and <em>Phlebotomus tobbi</em>.</span></p><p class="p2"><span class="s1">The known distributions of these species within the project area (Europe, the Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) assistance in targeting surveys to collect ­distribution data for those areas with no field validated information, and b) a first indication of project wide distributions.</span></p> Published on 2016-12-19 16:32:34https://openhealthdata.metajnl.com/article/10.5334/ohd.26Physical Activity and Respiratory Health (PhARaoH): Data from a Cross-Sectional Studyhttps://openhealthdata.metajnl.com/article/10.5334/ohd.28<p class="p1"><span class="s1">The dataset consists of a densely phenotyped sample of adults collected from March to August 2014. The dataset captures behavioural, physical, physiological and psychosocial characteristics of individuals with and without a General Practitioner diagnosis of chronic obstructive pulmonary disease (COPD). Data were collected at Glenfield Hospital on 436 individuals (139 COPD patients and 297 apparently healthy adults) aged 40–75 years, residing in Leicestershire and Rutland, United Kingdom. The dataset includes seven days of raw wrist-worn accelerometry, venous blood biomarkers, non-invasive point-of-care cardio-metabolic risk profiles, physical measures and questionnaire data.</span></p> Published on 2016-12-05 17:15:04https://openhealthdata.metajnl.com/article/10.5334/ohd.28Data from ‘Graphic Medicine’ as a Mental Health Information Resource: Insights from Comics Producershttps://openhealthdata.metajnl.com/article/10.5334/ohd.25<p class="p1"><span class="s1">This dataset contains the full text transcripts from 15 semi-structured interviews (approximately 44,100 words) conducted during November and December 2014 with participants involved in various aspects of the process of health-related comics production. These participants are authors and publishers and their work is publicly recognised in the comics community.</span></p><p class="p2"><span class="s1">The dataset has been deposited in the Open Health Data Dataverse repository as a zipped folder containing 15 individual simple text files corresponding to each interview and a ReadMe file containing contextual information and other metadata. </span></p><p class="p2"><span class="s1">An initial domain analysis of the interviews was published as Farthing, A., &amp; Priego, E. (2016). ‘Graphic Medicine’ as a Mental Health Information Resource: Insights from Comics Producers. <em>The Comics Grid: Journal of Comics Scholarship</em>, 6(1), 3. DOI: <a href="http://doi.org/10.16995/cg.74">http://doi.org/10.16995/cg.74</a></span></p> Published on 2016-08-30 10:38:12https://openhealthdata.metajnl.com/article/10.5334/ohd.25Next Steps (formerly known as the Longitudinal Study of Young People in England)https://openhealthdata.metajnl.com/article/10.5334/ohd.16<p class="p1">Next Steps (formerly known as the Longitudinal Study of Young People in England – LSYPE) is a longitudinal study which follows a sample of around 16,000 people born in 1989/1990. Study members were recruited via schools in England when they were aged 13–14 in 2004. They were interviewed annually for seven waves until they were aged 19/20 in 2010. Co-resident parents were also interviewed in the first four waves of the study.</p> Published on 2016-02-25 11:59:02https://openhealthdata.metajnl.com/article/10.5334/ohd.16The European Distribution of <i>Sus Scrofa</i>. Model Outputs from the Project Described within the Poster – Where are All the Boars? An Attempt to Gain a Continental Perspectivehttps://openhealthdata.metajnl.com/article/10.5334/ohd.24<p class="p1">Wild boar is a host of a number of arthropod-vectored diseases and its numbers are on the rise in mainland Europe. The species potentially impacts ecosystems, humans and farming practices and so its distribution is of interest to policy makers in a number of fields beyond that of the primarily epidemiological goal of this study.</p><p class="p3">Three statistical model outputs describing the distribution and abundance of the species Sus scrofa (Wild boar) are included in this data package. The extent of this dataset covers continental Europe. These data were presented as a poster [1] at the conference Genes, Ecosystems and Risk of Infection (GERI 2015).</p><p class="p3">The first of the three models provide a European map presenting the probability of presence of Sus scrofa, which can be used to describe the likely geographical distribution of the species. The second and third models provide indices to help describe the likely abundance across the continent. The two indices include “the proportion of suitable habitat where presence is estimated” and a simple classification of boar abundance across Europe using quantiles of existing abundance data and proxies.</p> Published on 2016-01-28 18:46:34https://openhealthdata.metajnl.com/article/10.5334/ohd.24Data from the PALS (Pregnancy and Lifestyle Study), a Community-Based Study of Lifestyle on Fertility and Reproductive Outcomehttps://openhealthdata.metajnl.com/article/10.5334/ohd.apIn order to assess the possible effects of lifestyle on fertility and pregnancy outcome, the PALS (Pregnancy and Lifestyle study) collected extensive data on a broad range of parameters termed ‘lifestyle’ from couples who were planning a natural (non-assisted) pregnancy in the coming months. There was no intervention. Participants were recruited over a six year period from 1988 to 1993 in response to extensive promotion in the local media. Male and female partners were interviewed independently and all interviews were conducted prospectively before the couple attempted to conceive. The result of each month of ‘trying’ was recorded and pregnancies were confirmed by urine tests and by ultrasound. The length of gestation of each pregnancy was recorded and pregnancies at term were classified with respect to weight. Multiple pregnancies and/or babies with congenital abnormalities have been excluded from the dataset. The data is stored as an xls file and each variable has a codename. For each of 582 couples there are 355 variables, the codes for which are described in a separate metadata file. The questionnaire based data includes information about households, occupation, chemical exposures at work and home, diet, smoking, alcohol use, hobbies, exercise and health. Recorded observations include monthly pregnancy tests and pregnancy outcomes. Published on 2015-11-10 10:51:11https://openhealthdata.metajnl.com/article/10.5334/ohd.apA Relational Database of WHO Mortality Data Prepared to Facilitate Global Mortality Researchhttps://openhealthdata.metajnl.com/article/10.5334/ohd.ao<p>Detailed world mortality data such as collected by the World Health Organization gives a wealth of information about causes of death worldwide over a time span of 60 year. However, the raw mortality data in text format as provided by the WHO is not directly suitable for systematic research and data mining. In this Data Paper, a relational database is presented that is created from the raw WHO mortality data set and includes mortality rates, an ICD-code table and country reference data. This enriched database, as a corpus of global mortality data, can be readily imported in relational databases but can also function as the data source for other types of databases. The use of this database can therefore greatly facilitate global epidemiological research that may provide new clues to genetic or environmental factors in the origins of diseases.</p> Published on 2015-09-30 14:02:35https://openhealthdata.metajnl.com/article/10.5334/ohd.ao